摘要
目的以胃肠道肿瘤患者的望诊图像特征为主要研究内容,分析胃肠道肿瘤患者的面舌象色度学参数的特征规律,建立胃肠道肿瘤辅助诊断模型。方法应用单因素方差分析、t检验、Mann-whitney U检验、典型相关分析方法和Spearman等统计方法分析391例对照组、359例胃肠道肿瘤患者的望诊图像指标特点及肿瘤标志物关联;采用支持向量机、随机森林、K邻近、朴素贝叶斯、极端梯度提升、自适应增强等机器学习方法建立胃肠道肿瘤疾病辅助诊断模型。结果在面象指标上,对照组、早期胃肠道肿瘤患者、中晚期胃肠道肿瘤患者F-R、F-G、F-B等指标均有差异;舌象特征比较,对照组、早期胃肠道肿瘤患者、中晚期胃肠道肿瘤患者TC-L、TB-L、TB-a呈逐步降低趋势;以自适应增强算法构建的基于面舌象色度参数胃肠道肿瘤疾病辅助诊断模型AUC为0.930。结论以面舌象构建的胃肠道疾病辅助诊断模型具有良好的诊断效果,可为深入探索面舌象与肿瘤病证诊断的可行性提供客观数据支持。
Objective To analyze the characteristics of facial and tongue chromaticity parameters in patients with gastrointestinal tumors by setting the inspection image characteristics of patients with gastrointestinal tumors as the main research content;To establish an auxiliary diagnostic model for gastrointestinal tumors.Methods One-way ANOVA,t-test,Mann-whitney U test,canonical correlation analysis and Spearman statistical methods were used to analyze the characteristics of inspection image indexes and correlation of tumor markers of the 391 cases in the control group and 359 patients with gastrointestinal tumors.Machine learning methods such as SVM,Random Forest,KNN,Naive Bayes,XG Boost and Ada Boost were used to establish an auxiliary diagnostic model for gastrointestinal tumors.Results In terms of facial indicators,there were differences in F-R,F-G and F-B indicators among the control group,early-stage gastrointestinal cancer patients,and mid-to late-stage gastrointestinal cancer patients,in the comparison of tongue features among,TC-L,TB-L and TB-a of the control group,patients with early gastrointestinal tumors,and patients with intermediate and advanced gastrointestinal tumors showed a gradual downward trend;the AUC of the auxiliary diagnosis model of gastrointestinal tumor disease based on the chromaticity parameters of face tongue image constructed by Ada Boost algorithm was 0.930.Conclusion The auxiliary diagnostic model of gastrointestinal diseases constructed by facial and tongue images has good diagnostic effect,which can provide objective data support for in-depth exploration of the complex relationship between diagnosis and disease.
作者
许晓妍
石玉琳
屠立平
江涛
焦文
胡晓娟
许家佗
XU Xiaoyan;SHI Yulin;TU Liping;JIANG Tao;JIAO Wen;HU Xiaojuan;XU Jiatuo(School of Traditional Chinese Medicine,Shanghai University of Traditional Chinese Medicine,Shanghai 201203,China;The Office of Academic Affairs,Shanghai University of Traditional Chinese Medicine,Shanghai 201203,China;Shanghai Municipality Hospital of Traditional Chinese Medicine,Shanghai University of Traditional Chinese Medicine,Shanghai 200071,China;Collaborative Innovation Center for Traditional Chinese Medicine Health Service,Shanghai University of Traditional Chinese Medicine,Shanghai 201203,China)
出处
《中国中医药信息杂志》
2025年第11期142-148,共7页
Chinese Journal of Information on Traditional Chinese Medicine
基金
国家自然科学基金(82305090)
上海市科学技术委员会启明星培育项目(22YF1448900)
中国博士后科学基金面上项目(2023M732337)
上海市“超级博士后”激励计划(2022509)。
关键词
望诊
面舌参数
胃肠道肿瘤
机器学习
疾病辅助诊断模型
inspection
facial tongue parameters
gastrointestinal tumors
machine learning
auxiliary disease diagnostic model
